基于食物链机制的动态多物种粒子群算法

doi:10.11772/j.issn.1001-9081.2016.05.1341

计算机应用 ›› 2016, Vol. 36 ›› Issue (5): 1341-1346.DOI: 10.11772/j.issn.1001-9081.2016.05.1341

基于食物链机制的动态多物种粒子群算法

刘角, 马迪, 马腾波, 张玮

太原理工大学化学化工学院, 太原 154300

收稿日期:2015-11-23 修回日期:2016-01-12 出版日期:2016-05-10 发布日期:2016-05-09
通讯作者: 张玮
作者简介:刘角(1991-),男,黑龙江佳木斯人,硕士研究生,主要研究方向:进化算法;马迪(1989-),男,河北衡水人,硕士研究生,主要研究方向:进化算法;马腾波(1990-),男,山西运城人,硕士研究生,主要研究方向:生产过程监测;张玮(1973-),女,山西太原人,副教授,博士,主要研究方向:进化算法,生产过程监测。
基金资助:
山西省自然科学基金资助项目(2015011019)。

Dynamic multi-species particle swarm optimization based on food chain mechanism

LIU Jiao, MA Di, MA Tengbo, ZHANG Wei

College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan Shanxi 030024, China

Received:2015-11-23 Revised:2016-01-12 Online:2016-05-10 Published:2016-05-09
Supported by:
This work is partially supported by the Natural Science Foundation of Shanxi Province (2015011019).

摘要/Abstract

摘要： 针对粒子群优化(PSO)算法在解决多峰函数时容易陷入局部最优的问题,提出了一种基于食物链机制的动态多物种粒子群(DSPSO)算法。受生物界的启发,引入食物链机制来保证种群的多样性,并结合繁殖机制使得算法具有良好的优化性能。食物链机制中,整个标榜群被分为几个子种群,每个子种群都能够捕食另外一个子种群。通过一定概率发生的捕食现象使得标榜群得以进化,剔除对种群贡献小的粒子,并通过繁殖策略生成新的粒子。种群通过不断地进化保证了种群的多样性,同时通过剔除较差粒子的误导作用使算法的进化更有效率。为了验证算法的有效性,选择了包括偏移函数、旋转函数在内的10个测试函数来测试DSPSO算法的性能。实验结果表明DSPSO算法有着良好的寻优性能。与PSO、局部版本的粒子群(LPSO)算法、动态多群粒子群(DMS-PSO)算法和全面学习粒子群(CLPSO)算法相比,DSPSO算法不仅能够得到较高精度的解,而且还具有较高的可信度。

关键词: 粒子群优化算法, 食物链机制, 动态多物种

Abstract: a novel Dynamic multi-Species Particle Swarm Optimization (DSPSO) algorithm based on food chain mechanism was proposed aiming at the problem that the basic Particle Swarm Optimization (PSO) algorithm is easy fall into local optimal solution when solving multimodal problems. Inspired by the natural ecosystem, a food chain mechanism and a reproduction mechanism were employed to keep the swarm diversity and good performance. In food chain mechanism, the swarm was divided into several sub-swarms, and each sub-swarm could prey on the others. The memory leader swarm was evolved and the less contributed particle was eliminated through predation, and then the new particle was generated through reproduction mechanism. The diversity was kept through the evaluation of the swarm, and the efficiency of the algorithm was enhanced through eliminating the misleading effect of the less contributed particles. In order to verify the effectiveness of the algorithm, ten benchmark problems including shifted problems and rotated problems were chose to test the performance of DSPSO. The experimental results show that DSPSO has a well optimizing performance. Compared with PSO algorithm, Local version Particle Swarm Optimization (LPSO) algorithm, Dynamic Multi-Swarm Particle Swarm Optimization (DMS-PSO) algorithm and Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm, DSPSO algorithm not only obtains more accurate solutions, but also has higher reliability.

Key words: Particle Swarm Optimization (PSO) algorithm, food chain mechanism, dynamic multi-species

中图分类号:

TP301.6

刘角, 马迪, 马腾波, 张玮. 基于食物链机制的动态多物种粒子群算法[J]. 计算机应用, 2016, 36(5): 1341-1346.

LIU Jiao, MA Di, MA Tengbo, ZHANG Wei. Dynamic multi-species particle swarm optimization based on food chain mechanism[J]. Journal of Computer Applications, 2016, 36(5): 1341-1346.

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基于食物链机制的动态多物种粒子群算法

Dynamic multi-species particle swarm optimization based on food chain mechanism

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